def main():
global args, logger
args = get_parser().parse_args()
logger = get_logger()
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h - 1) % 8 == 0 and (args.crop_w - 1) % 8 == 0
assert args.split in ['train', 'val', 'test']
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
train_transform = transforms.Compose([
transforms.RandScale([0.5, 2]),
transforms.RandRotate([-10, 10], padding=mean, ignore_label=args.ignore_label),
transforms.RandomGaussianBlur(),
transforms.RandomHorizontalFlip(),
transforms.Crop([args.crop_h, args.crop_w], crop_type='rand', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor()])
val_transform = transforms.Compose([transforms.Crop([args.crop_h, args.crop_w], crop_type='center', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor()])
val_data1 = datasets.SegData(split='train', data_root=args.data_root, data_list=args.val_list1, transform=val_transform)
val_loader1 = torch.utils.data.DataLoader(val_data1, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True)
from pspnet import PSPNet
model = PSPNet(backbone = args.backbone, layers=args.layers, classes=args.classes, zoom_factor=args.zoom_factor, use_softmax=False, use_aux=False, pretrained=False, syncbn=False).cuda()
logger.info(model)
# model = torch.nn.DataParallel(model).cuda()
model = model.cuda()
cudnn.enabled = True
cudnn.benchmark = True
if os.path.isfile(args.model_path):
logger.info("=> loading checkpoint '{}'".format(args.model_path))
checkpoint = torch.load(args.model_path)
# model.load_state_dict(checkpoint['state_dict'], strict=False)
# logger.info("=> loaded checkpoint '{}'".format(args.model_path))
pretrained_dict = {k.replace('module.',''): v for k, v in checkpoint['state_dict'].items()}
dict1 = model.state_dict()
model.load_state_dict(pretrained_dict, strict=False)
else:
raise RuntimeError("=> no checkpoint found at '{}'".format(args.model_path))
cv2.setNumThreads(0)
validate(val_loader1, val_data1.data_list, model, args.classes, mean, std, args.base_size1, args.crop_h, args.crop_w, args.scales)
def main():
global args, logger, writer
args = get_parser().parse_args()
import multiprocessing as mp
if mp.get_start_method(allow_none=True) != 'spawn':
mp.set_start_method('spawn', force=True)
rank, world_size = dist_init(args.port)
logger = get_logger()
writer = SummaryWriter(args.save_path)
#os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
#if len(args.gpu) == 1:
# args.syncbn = False
if rank == 0:
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h-1) % 8 == 0 and (args.crop_w-1) % 8 == 0
assert args.net_type in [0, 1, 2, 3]
if args.bn_group == 1:
args.bn_group_comm = None
else:
assert world_size % args.bn_group == 0
args.bn_group_comm = simple_group_split(world_size, rank, world_size // args.bn_group)
if rank == 0:
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
if args.net_type == 0:
from pspnet import PSPNet
model = PSPNet(backbone=args.backbone, layers=args.layers, classes=args.classes, zoom_factor=args.zoom_factor, syncbn=args.syncbn, group_size=args.bn_group, group=args.bn_group_comm).cuda()
elif args.net_type in [1, 2, 3]:
from pspnet_div4 import PSPNet
model = PSPNet(backbone=args.backbone, layers=args.layers, classes=args.classes, zoom_factor=args.zoom_factor, syncbn=args.syncbn, group_size=args.bn_group, group=args.bn_group_comm, net_type=args.net_type).cuda()
logger.info(model)
# optimizer = torch.optim.SGD(model.parameters(), args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
# newly introduced layer with lr x10
if args.net_type == 0:
optimizer = torch.optim.SGD(
[{'params': model.layer0.parameters()},
{'params': model.layer1.parameters()},
{'params': model.layer2.parameters()},
{'params': model.layer3.parameters()},
{'params': model.layer4.parameters()},
{'params': model.ppm.parameters(), 'lr': args.base_lr * 10},
{'params': model.conv6.parameters(), 'lr': args.base_lr * 10},
{'params': model.conv1_1x1.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls.parameters(), 'lr': args.base_lr * 10},
{'params': model.aux.parameters(), 'lr': args.base_lr * 10}],
lr=args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.net_type == 1:
optimizer = torch.optim.SGD(
[{'params': model.layer0.parameters()},
{'params': model.layer1.parameters()},
{'params': model.layer2.parameters()},
{'params': model.layer3.parameters()},
{'params': model.layer4.parameters()},
{'params': model.layer4_p.parameters()},
{'params': model.ppm.parameters(), 'lr': args.base_lr * 10},
{'params': model.ppm_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.aux.parameters(), 'lr': args.base_lr * 10}],
lr=args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.net_type == 2:
optimizer = torch.optim.SGD(
[{'params': model.layer0.parameters()},
{'params': model.layer1.parameters()},
{'params': model.layer2.parameters()},
{'params': model.layer3.parameters()},
{'params': model.layer4.parameters()},
{'params': model.layer4_p.parameters()},
{'params': model.ppm.parameters(), 'lr': args.base_lr * 10},
{'params': model.ppm_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.att.parameters(), 'lr': args.base_lr * 10},
{'params': model.att_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.aux.parameters(), 'lr': args.base_lr * 10}],
lr=args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
elif args.net_type == 3:
optimizer = torch.optim.SGD(
[{'params': model.layer0.parameters()},
{'params': model.layer1.parameters()},
{'params': model.layer2.parameters()},
{'params': model.layer3.parameters()},
{'params': model.layer4.parameters()},
{'params': model.layer4_p.parameters()},
{'params': model.ppm.parameters(), 'lr': args.base_lr * 10},
{'params': model.ppm_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls.parameters(), 'lr': args.base_lr * 10},
{'params': model.cls_p.parameters(), 'lr': args.base_lr * 10},
{'params': model.att.parameters(), 'lr': args.base_lr * 10},
{'params': model.aux.parameters(), 'lr': args.base_lr * 10}],
lr=args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
fcw = V11RFCN()
fcw_model = torch.load('checkpoint_e8.pth')['state_dict']
fcw_dict = fcw.state_dict()
pretrained_fcw = {k: v for k, v in fcw_model.items() if k in fcw_dict}
fcw_dict.update(pretrained_fcw)
fcw.load_state_dict(fcw_dict)
#fcw = DistModule(fcw)
#print(fcw)
fcw = fcw.cuda()
#model = torch.nn.DataParallel(model).cuda()
model = DistModule(model)
#if args.syncbn:
# from lib.syncbn import patch_replication_callback
# patch_replication_callback(model)
cudnn.enabled = True
cudnn.benchmark = True
criterion = nn.NLLLoss(ignore_index=args.ignore_label).cuda()
if args.weight:
def map_func(storage, location):
return storage.cuda()
if os.path.isfile(args.weight):
logger.info("=> loading weight '{}'".format(args.weight))
checkpoint = torch.load(args.weight, map_location=map_func)['state_dict']
checkpoint = {k: v for k, v in checkpoint.items() if 'ppm' not in k}
model_dict = model.state_dict()
model_dict.update(checkpoint)
model.load_state_dict(model_dict)
logger.info("=> loaded weight '{}'".format(args.weight))
else:
logger.info("=> no weight found at '{}'".format(args.weight))
if args.resume:
load_state(args.resume, model, optimizer)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
train_transform = transforms.Compose([
transforms.RandScale([args.scale_min, args.scale_max]),
#transforms.RandRotate([args.rotate_min, args.rotate_max], padding=mean, ignore_label=args.ignore_label),
transforms.RandomGaussianBlur(),
transforms.RandomHorizontalFlip(),
transforms.Crop([args.crop_h, args.crop_w], crop_type='rand', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)])
train_data = datasets.SegData(split='train', data_root=args.data_root, data_list=args.train_list, transform=train_transform)
train_sampler = DistributedSampler(train_data)
train_loader = torch.utils.data.DataLoader(train_data, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=False, sampler=train_sampler)
if args.evaluate:
val_transform = transforms.Compose([
transforms.Crop([args.crop_h, args.crop_w], crop_type='center', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)])
val_data = datasets.SegData(split='val', data_root=args.data_root, data_list=args.val_list, transform=val_transform)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=args.batch_size_val, shuffle=False, num_workers=args.workers, pin_memory=True)
for epoch in range(args.start_epoch, args.epochs + 1):
loss_train, mIoU_train, mAcc_train, allAcc_train = train(train_loader, model, criterion, optimizer, epoch, args.zoom_factor, args.batch_size, args.aux_weight, fcw)
if rank == 0:
writer.add_scalar('loss_train', loss_train, epoch)
writer.add_scalar('mIoU_train', mIoU_train, epoch)
writer.add_scalar('mAcc_train', mAcc_train, epoch)
writer.add_scalar('allAcc_train', allAcc_train, epoch)
# write parameters histogram costs lots of time
# for name, param in model.named_parameters():
# writer.add_histogram(name, param, epoch)
if epoch % args.save_step == 0 and rank == 0:
filename = args.save_path + '/train_epoch_' + str(epoch) + '.pth'
logger.info('Saving checkpoint to: ' + filename)
torch.save({'epoch': epoch, 'state_dict': model.state_dict(), 'optimizer': optimizer.state_dict()}, filename)
#if epoch / args.save_step > 2:
# deletename = args.save_path + '/train_epoch_' + str(epoch - args.save_step*2) + '.pth'
# os.remove(deletename)
if args.evaluate:
loss_val, mIoU_val, mAcc_val, allAcc_val = validate(val_loader, model, criterion, args.classes, args.zoom_factor)
writer.add_scalar('loss_val', loss_val, epoch)
writer.add_scalar('mIoU_val', mIoU_val, epoch)
writer.add_scalar('mAcc_val', mAcc_val, epoch)
writer.add_scalar('allAcc_val', allAcc_val, epoch)
def main():
global args, logger, writer
args = get_parser().parse_args()
logger = get_logger()
writer = SummaryWriter(args.save_path)
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
if args.dist:
dist_init(args.port, backend=args.backend)
if len(args.gpu) == 1:
args.syncbn = False
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h - 1) % 8 == 0 and (args.crop_w - 1) % 8 == 0
world_size = 1
rank = 0
if args.dist:
rank = dist.get_rank()
world_size = dist.get_world_size()
if rank == 0:
logger.info('dist:{}'.format(args.dist))
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
# rank = dist.get_rank()
if args.bn_group > 1:
args.syncbn = True
bn_sync_stats = True
bn_group_comm = simple_group_split(world_size, rank,
world_size // args.bn_group)
else:
args.syncbn = False
bn_sync_stats = False
bn_group_comm = None
model = PSPNet(layers=args.layers,
classes=args.classes,
zoom_factor=args.zoom_factor,
syncbn=args.syncbn,
group_size=args.bn_group,
group=bn_group_comm,
sync_stats=bn_sync_stats)
if rank == 0:
logger.info(model)
# optimizer = torch.optim.SGD(model.parameters(), args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
# newly introduced layer with lr x10
optimizer = torch.optim.SGD([{
'params': model.layer0.parameters()
}, {
'params': model.layer1.parameters()
}, {
'params': model.layer2.parameters()
}, {
'params': model.layer3.parameters()
}, {
'params': model.layer4.parameters()
}, {
'params': model.ppm.parameters(),
'lr': args.base_lr * 10
}, {
'params': model.cls.parameters(),
'lr': args.base_lr * 10
}, {
'params': model.aux.parameters(),
'lr': args.base_lr * 10
}],
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# model = torch.nn.DataParallel(model).cuda()
# if args.syncbn:
# from lib.syncbn import patch_replication_callback
# patch_replication_callback(model)
model = model.cuda()
cudnn.enabled = True
cudnn.benchmark = True
criterion = nn.NLLLoss(ignore_index=args.ignore_label)
if args.weight:
def map_func(storage, location):
return storage.cuda()
if os.path.isfile(args.weight):
logger.info("=> loading weight '{}'".format(args.weight))
checkpoint = torch.load(args.weight, map_location=map_func)
model.load_state_dict(checkpoint['state_dict'])
logger.info("=> loaded weight '{}'".format(args.weight))
else:
logger.info("=> no weight found at '{}'".format(args.weight))
if args.resume:
if os.path.isfile(args.resume):
logger.info("=> loading checkpoint '{}'".format(args.resume))
# checkpoint = torch.load(args.resume)
# args.start_epoch = checkpoint['epoch']
# model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
model, optimizer, args.start_epoch = restore_from(
model, optimizer, args.resume)
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, args.start_epoch))
else:
logger.info("=> no checkpoint found at '{}'".format(args.resume))
if args.dist:
broadcast_params(model)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
train_transform = transforms.Compose([
transforms.RandScale([args.scale_min, args.scale_max]),
transforms.RandRotate([args.rotate_min, args.rotate_max],
padding=mean,
ignore_label=args.ignore_label),
transforms.RandomGaussianBlur(),
transforms.RandomHorizontalFlip(),
transforms.Crop([args.crop_h, args.crop_w],
crop_type='rand',
padding=mean,
ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
train_data = datasets.SegData(split='train',
data_root=args.data_root,
data_list=args.train_list,
transform=train_transform)
train_sampler = None
if args.dist:
train_sampler = DistributedSampler(train_data)
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
shuffle=False if train_sampler else True,
num_workers=args.workers,
pin_memory=False,
sampler=train_sampler)
if args.evaluate:
val_transform = transforms.Compose([
transforms.Crop([args.crop_h, args.crop_w],
crop_type='center',
padding=mean,
ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
val_data = datasets.SegData(split='val',
data_root=args.data_root,
data_list=args.val_list,
transform=val_transform)
val_sampler = None
if args.dist:
val_sampler = DistributedSampler(val_data)
val_loader = torch.utils.data.DataLoader(
val_data,
batch_size=args.batch_size_val,
shuffle=False,
num_workers=args.workers,
pin_memory=False,
sampler=val_sampler)
for epoch in range(args.start_epoch, args.epochs + 1):
loss_train, mIoU_train, mAcc_train, allAcc_train = train(
train_loader, model, criterion, optimizer, epoch, args.zoom_factor,
args.batch_size, args.aux_weight)
writer.add_scalar('loss_train', loss_train.cpu().numpy(), epoch)
writer.add_scalar('mIoU_train', mIoU_train, epoch)
writer.add_scalar('mAcc_train', mAcc_train, epoch)
writer.add_scalar('allAcc_train', allAcc_train, epoch)
# write parameters histogram costs lots of time
# for name, param in model.named_parameters():
# writer.add_histogram(name, param, epoch)
if args.evaluate and rank == 0:
loss_val, mIoU_val, mAcc_val, allAcc_val = validate(
val_loader, model, criterion, args.classes, args.zoom_factor)
writer.add_scalar('loss_val', loss_val.cpu().numpy(), epoch)
writer.add_scalar('mIoU_val', mIoU_val, epoch)
writer.add_scalar('mAcc_val', mAcc_val, epoch)
writer.add_scalar('allAcc_val', allAcc_val, epoch)
if epoch % args.save_step == 0 and (rank == 0):
filename = args.save_path + '/train_epoch_' + str(epoch) + '.pth'
logger.info('Saving checkpoint to: ' + filename)
torch.save(
{
'epoch': epoch,
'state_dict': model.cpu().state_dict(),
'optimizer': optimizer.state_dict()
}, filename)
def main():
"""Create the model and start the training."""
with open(args.config) as f:
config = yaml.load(f)
for k, v in config['common'].items():
setattr(args, k, v)
mkdirs(osp.join("logs/"+args.exp_name))
logger = create_logger('global_logger', "logs/" + args.exp_name + '/log.txt')
logger.info('{}'.format(args))
##############################
for key, val in vars(args).items():
logger.info("{:16} {}".format(key, val))
logger.info("random_scale {}".format(args.random_scale))
logger.info("is_training {}".format(args.is_training))
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
h, w = map(int, args.input_size_target.split(','))
input_size_target = (h, w)
print(type(input_size_target[1]))
cudnn.enabled = True
args.snapshot_dir = args.snapshot_dir + args.exp_name
tb_logger = SummaryWriter("logs/"+args.exp_name)
##############################
#validation data
h, w = map(int, args.input_size_test.split(','))
input_size_test = (h,w)
h, w = map(int, args.com_size.split(','))
com_size = (h, w)
h, w = map(int, args.input_size_crop.split(','))
input_size_crop = h,w
h,w = map(int, args.input_size_target_crop.split(','))
input_size_target_crop = h,w
test_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_transform = transforms.Compose([
transforms.Resize((input_size_test[1], input_size_test[0])),
transforms.ToTensor(),
test_normalize])
valloader = data.DataLoader(cityscapesDataSet(
args.data_dir_target,
args.data_list_target_val,
crop_size=input_size_test,
set='train',
transform=test_transform),num_workers=args.num_workers,
batch_size=1, shuffle=False, pin_memory=True)
with open('./dataset/cityscapes_list/info.json', 'r') as fp:
info = json.load(fp)
mapping = np.array(info['label2train'], dtype=np.int)
label_path_list_val = args.label_path_list_val
label_path_list_test = args.label_path_list_test
label_path_list_test = './dataset/cityscapes_list/label.txt'
gt_imgs_val = open(label_path_list_val, 'r').read().splitlines()
gt_imgs_val = [osp.join(args.data_dir_target_val, x) for x in gt_imgs_val]
testloader = data.DataLoader(cityscapesDataSet(
args.data_dir_target,
args.data_list_target_test,
crop_size=input_size_test,
set='val',
transform=test_transform),
num_workers=args.num_workers,
batch_size=1,
shuffle=False, pin_memory=True)
gt_imgs_test = open(label_path_list_test ,'r').read().splitlines()
gt_imgs_test = [osp.join(args.data_dir_target_test, x) for x in gt_imgs_test]
name_classes = np.array(info['label'], dtype=np.str)
interp_val = nn.Upsample(size=(com_size[1], com_size[0]),mode='bilinear', align_corners=True)
####
#build model
####
builder = ModelBuilder()
net_encoder = builder.build_encoder(
arch=args.arch_encoder,
fc_dim=args.fc_dim,
weights=args.weights_encoder)
net_decoder = builder.build_decoder(
arch=args.arch_decoder,
fc_dim=args.fc_dim,
num_class=args.num_classes,
weights=args.weights_decoder,
use_aux=True)
model = SegmentationModule(
net_encoder, net_decoder, args.use_aux)
if args.num_gpus > 1:
model = torch.nn.DataParallel(model)
patch_replication_callback(model)
model.cuda()
nets = (net_encoder, net_decoder, None, None)
optimizers = create_optimizer(nets, args)
cudnn.enabled=True
cudnn.benchmark=True
model.train()
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
source_normalize = transforms_seg.Normalize(mean=mean,
std=std)
mean_mapping = [0.485, 0.456, 0.406]
mean_mapping = [item * 255 for item in mean_mapping]
if not os.path.exists(args.snapshot_dir):
os.makedirs(args.snapshot_dir)
source_transform = transforms_seg.Compose([
transforms_seg.Resize([input_size[1], input_size[0]]),
segtransforms.RandScale((args.scale_min, args.scale_max)),
#segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label),
#segtransforms.RandomGaussianBlur(),
segtransforms.RandomHorizontalFlip(),
segtransforms.Crop([input_size_crop[1], input_size_crop[0]], crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label),
transforms_seg.ToTensor(),
source_normalize])
target_normalize = transforms_seg.Normalize(mean=mean,
std=std)
target_transform = transforms_seg.Compose([
transforms_seg.Resize([input_size_target[1], input_size_target[0]]),
segtransforms.RandScale((args.scale_min, args.scale_max)),
#segtransforms.RandRotate((args.rotate_min, args.rotate_max), padding=mean_mapping, ignore_label=args.ignore_label),
#segtransforms.RandomGaussianBlur(),
segtransforms.RandomHorizontalFlip(),
segtransforms.Crop([input_size_target_crop[1], input_size_target_crop[0]],crop_type='rand', padding=mean_mapping, ignore_label=args.ignore_label),
transforms_seg.ToTensor(),
target_normalize])
trainloader = data.DataLoader(
GTA5DataSet(args.data_dir, args.data_list, max_iters=args.num_steps * args.iter_size * args.batch_size,
crop_size=input_size, transform = source_transform),
batch_size=args.batch_size, shuffle=True, num_workers=1, pin_memory=True)
trainloader_iter = enumerate(trainloader)
targetloader = data.DataLoader(fake_cityscapesDataSet(args.data_dir_target, args.data_list_target,
max_iters=args.num_steps * args.iter_size * args.batch_size,
crop_size=input_size_target,
set=args.set,
transform=target_transform),
batch_size=args.batch_size, shuffle=True, num_workers=1,
pin_memory=True)
targetloader_iter = enumerate(targetloader)
# implement model.optim_parameters(args) to handle different models' lr setting
criterion_seg = torch.nn.CrossEntropyLoss(ignore_index=255,reduce=False)
interp_target = nn.Upsample(size=(input_size_target[1], input_size_target[0]), align_corners=True, mode='bilinear')
# labels for adversarial training
source_label = 0
target_label = 1
optimizer_encoder, optimizer_decoder, optimizer_disc, optimizer_reconst = optimizers
batch_time = AverageMeter(10)
loss_seg_value1 = AverageMeter(10)
is_best_test = True
best_mIoUs = 0
loss_seg_value2 = AverageMeter(10)
loss_balance_value = AverageMeter(10)
loss_pseudo_value = AverageMeter(10)
bounding_num = AverageMeter(10)
pseudo_num = AverageMeter(10)
for i_iter in range(args.num_steps):
# train G
# don't accumulate grads in D
end = time.time()
_, batch = trainloader_iter.__next__()
images, labels, _ = batch
images = Variable(images).cuda(async=True)
labels = Variable(labels).cuda(async=True)
seg, aux_seg, loss_seg2, loss_seg1 = model(images, labels)
loss_seg2 = torch.mean(loss_seg2)
loss_seg1 = torch.mean(loss_seg1)
loss = loss_seg2+args.lambda_seg*loss_seg1
#logger.info(loss_seg1.data.cpu().numpy())
loss_seg_value2.update(loss_seg2.data.cpu().numpy())
# train with target
optimizer_encoder.zero_grad()
optimizer_decoder.zero_grad()
loss.backward()
optimizer_encoder.step()
optimizer_decoder.step()
del seg, loss_seg2
_, batch = targetloader_iter.__next__()
with torch.no_grad():
images, labels, _ = batch
images = Variable(images).cuda(async=True)
result = model(images, None)
del result
batch_time.update(time.time() - end)
remain_iter = args.num_steps - i_iter
remain_time = remain_iter * batch_time.avg
t_m, t_s = divmod(remain_time, 60)
t_h, t_m = divmod(t_m, 60)
remain_time = '{:02d}:{:02d}:{:02d}'.format(int(t_h), int(t_m), int(t_s))
adjust_learning_rate(optimizer_encoder, i_iter, args.lr_encoder, args)
adjust_learning_rate(optimizer_decoder, i_iter, args.lr_decoder, args)
if i_iter % args.print_freq == 0:
lr_encoder = optimizer_encoder.param_groups[0]['lr']
lr_decoder = optimizer_decoder.param_groups[0]['lr']
logger.info('exp = {}'.format(args.snapshot_dir))
logger.info('Iter = [{0}/{1}]\t'
'Time = {batch_time.avg:.3f}\t'
'loss_seg1 = {loss_seg1.avg:4f}\t'
'loss_seg2 = {loss_seg2.avg:.4f}\t'
'lr_encoder = {lr_encoder:.8f} lr_decoder = {lr_decoder:.8f}'.format(
i_iter, args.num_steps, batch_time=batch_time,
loss_seg1=loss_seg_value1, loss_seg2=loss_seg_value2,
lr_encoder=lr_encoder,
lr_decoder=lr_decoder))
logger.info("remain_time: {}".format(remain_time))
if not tb_logger is None:
tb_logger.add_scalar('loss_seg_value1', loss_seg_value1.avg, i_iter)
tb_logger.add_scalar('loss_seg_value2', loss_seg_value2.avg, i_iter)
tb_logger.add_scalar('lr', lr_encoder, i_iter)
#####
#save image result
if i_iter % args.save_pred_every == 0 and i_iter != 0:
logger.info('taking snapshot ...')
model.eval()
val_time = time.time()
hist = np.zeros((19,19))
for index, batch in tqdm(enumerate(valloader)):
with torch.no_grad():
image, name = batch
output2, _ = model(Variable(image).cuda(), None)
pred = interp_val(output2)
del output2
pred = pred.cpu().data[0].numpy()
pred = pred.transpose(1, 2, 0)
pred = np.asarray(np.argmax(pred, axis=2), dtype=np.uint8)
label = np.array(Image.open(gt_imgs_val[index]))
#label = np.array(label.resize(com_size, Image.
label = label_mapping(label, mapping)
#logger.info(label.shape)
hist += fast_hist(label.flatten(), pred.flatten(), 19)
mIoUs = per_class_iu(hist)
for ind_class in range(args.num_classes):
logger.info('===>' + name_classes[ind_class] + ':\t' + str(round(mIoUs[ind_class] * 100, 2)))
tb_logger.add_scalar(name_classes[ind_class] + '_mIoU', mIoUs[ind_class], i_iter)
mIoUs = round(np.nanmean(mIoUs) *100, 2)
if mIoUs >= best_mIoUs:
is_best_test = True
best_mIoUs = mIoUs
else:
is_best_test = False
logger.info("current mIoU {}".format(mIoUs))
logger.info("best mIoU {}".format(best_mIoUs))
tb_logger.add_scalar('val mIoU', mIoUs, i_iter)
tb_logger.add_scalar('val mIoU', mIoUs, i_iter)
net_encoder, net_decoder, net_disc, net_reconst = nets
save_checkpoint(net_encoder, 'encoder', i_iter, args, is_best_test)
save_checkpoint(net_decoder, 'decoder', i_iter, args, is_best_test)
model.train()